Recognition of sketching from surface electromyography

A preliminary study of classification of upper limb motions and forces based on mechanomyography

Rehabilitation training is essential for patients who have a history of certain illnesses, such as stroke. As a crucial part of rehabilitation training, upper limb training involves such key factors as upper limb motions and forces. This study investigated three upper limb motions (elbow flexion of 135°, Motion 1; shoulder flexion of 90°, Motion 2; and shoulder abduction of 90°, Motion 3) and various forces (muscle Force 0, no force; holding one 1.4 kg dumbbell, muscle Force 1; holding one 2.4 kg dumbbell, muscle Force 2) in combination to evaluate nine motion patterns. These patterns were completed by twelve healthy volunteers. Mechanomyography (MMG) measurements of the biceps brachii (Channel 1), triceps (Channel 2), and deltoid (Channel 3) muscles were collected. These were subsequently divided into signal segments corresponding to each of the motions using a segmentation method based on average energy. After extracting time-domain features and wavelet packet energy features, support vector machine analysis (SVM) was used for the classification of the upper limb motions and forces based on the MMG measurements. Channel 2 and Channel 3 were shown to play an important role in the classification of upper limb motions, and Channel 1 played a role in the classification of the forces. These results demonstrate that collection of MMG measurements from the three muscles is feasible and suggest a foundation for further studies in which rehabilitation training is evaluated based on MMG measurements.

An efficient approach for physical actions classification using surface EMG signals

Physical actions classification of surface electromyography (sEMG) signal is required in applications like prosthesis, and robotic control etc. In this paper, tunable-Q factor wavelet transform (TQWT) based algorithm is proposed for the classification of physical actions such as clapping, hugging, bowing, handshaking, standing, running, jumping, waving, seating, and walking. sEMG signal is decomposed into sub-bands by TQWT. Various features are extracted from each different band and statistical analysis is performed. These features are fed into multi-class least squares support vector machine classifier using two non-linear kernel functions, morlet wavelet function, and radial basis function. The proposed method is an attempt for classifying physical actions using TQWT and its performance and results are promising and have high classification accuracy of 97.74% for sub-band eight with morlet kernel function.